Group 26 - Assignment 5

Carl Felix Freiesleben - s203521
Emilie Munk - s203538
Josefine Løken - s183784
Judith Tierno Martí - s222869
Sahand Yazdani - s203538

Introduction

The analysis was performed on the dataset: Right Heart Catheterization (RHC) Dataset, first analysed Connors (et. al) (1996)

Before cleaning and augmentation:

  • 5735 patiens

  • 62 attributes

After cleaning and augmentation:

  • 5612 patiens

  • 53 attributes

Contains patient-, socioeconomic-, physiological-, disease-, and survival information.

Materials and Methods

We performed our analysis using \(\color{red}{\text{Tidyverse}}\).

Materials and Methods (exploratory dataviz)

Table 1

rhc_aug |> mutate(sex = factor(sex),
                    swang1 = factor(swang1),
                    death = factor(x = death, levels = c(0,1), c("Alive","Dead"))) |> 
  table1(x = formula(~ sex + age + race + swang1 | death),
         data = _)
Alive
(N=1972)
Dead
(N=3640)
Overall
(N=5612)
sex
Female 906 (45.9%) 1594 (43.8%) 2500 (44.5%)
Male 1066 (54.1%) 2046 (56.2%) 3112 (55.5%)
age
Mean (SD) 56.6 (17.4) 64.0 (15.7) 61.4 (16.7)
Median [Min, Max] 58.0 [18.0, 102] 66.0 [18.0, 101] 64.0 [18.0, 102]
race
black 323 (16.4%) 577 (15.9%) 900 (16.0%)
other 121 (6.1%) 223 (6.1%) 344 (6.1%)
white 1528 (77.5%) 2840 (78.0%) 4368 (77.8%)
swang1
0 1291 (65.5%) 2177 (59.8%) 3468 (61.8%)
1 681 (34.5%) 1463 (40.2%) 2144 (38.2%)

Investigating the mean blood pressure of the patients admitted with different diseases

#:::: {.columns}

::: {.column width=“50%”} ::: {.column width=“50%”}

::: {.column width=“50%”} Violin plot \(\rightarrow\) Multimodal / Bimodal distribution

::: {.column width=“50%”}

:::: {.columns}

Plots

:::: {.columns}

:::: {.columns}

PCA

Modelling

More beautiful plots by Emilie

Discussion

How come we found no major discoveries?

What could have been done differently?

Conclusion

We can conclude that PC can make sense for further analysis.

We can conclude that high values of APS for several diagnosis, will increase the risk of death